Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations201
Missing cells5
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.8 KiB
Average record size in memory773.5 B

Variable types

Numeric18
Categorical12

Alerts

aspiration is highly overall correlated with compression-ratio and 2 other fieldsHigh correlation
body-style is highly overall correlated with height and 1 other fieldsHigh correlation
bore is highly overall correlated with city-L/100km and 10 other fieldsHigh correlation
city-L/100km is highly overall correlated with bore and 11 other fieldsHigh correlation
city-mpg is highly overall correlated with bore and 10 other fieldsHigh correlation
compression-ratio is highly overall correlated with aspiration and 4 other fieldsHigh correlation
curb-weight is highly overall correlated with bore and 10 other fieldsHigh correlation
diesel is highly overall correlated with compression-ratio and 4 other fieldsHigh correlation
drive-wheels is highly overall correlated with makeHigh correlation
engine-location is highly overall correlated with engine-size and 4 other fieldsHigh correlation
engine-size is highly overall correlated with bore and 14 other fieldsHigh correlation
engine-type is highly overall correlated with engine-size and 3 other fieldsHigh correlation
engine_hp_ratio is highly overall correlated with aspiration and 10 other fieldsHigh correlation
fuel-system is highly overall correlated with aspiration and 6 other fieldsHigh correlation
gas is highly overall correlated with compression-ratio and 4 other fieldsHigh correlation
height is highly overall correlated with body-style and 4 other fieldsHigh correlation
highway-mpg is highly overall correlated with bore and 12 other fieldsHigh correlation
horsepower is highly overall correlated with bore and 12 other fieldsHigh correlation
horsepower-binned is highly overall correlated with city-L/100km and 10 other fieldsHigh correlation
length is highly overall correlated with bore and 11 other fieldsHigh correlation
make is highly overall correlated with bore and 11 other fieldsHigh correlation
num-of-cylinders is highly overall correlated with city-L/100km and 8 other fieldsHigh correlation
num-of-doors is highly overall correlated with body-style and 2 other fieldsHigh correlation
peak-rpm is highly overall correlated with diesel and 2 other fieldsHigh correlation
price is highly overall correlated with bore and 10 other fieldsHigh correlation
stroke is highly overall correlated with engine-location and 1 other fieldsHigh correlation
symboling is highly overall correlated with height and 2 other fieldsHigh correlation
wheel-base is highly overall correlated with bore and 10 other fieldsHigh correlation
width is highly overall correlated with bore and 11 other fieldsHigh correlation
engine-location is highly imbalanced (88.8%) Imbalance
num-of-cylinders is highly imbalanced (58.6%) Imbalance
diesel is highly imbalanced (53.3%) Imbalance
gas is highly imbalanced (53.3%) Imbalance
stroke has 4 (2.0%) missing values Missing
symboling has 65 (32.3%) zeros Zeros

Reproduction

Analysis started2025-03-13 11:12:09.282031
Analysis finished2025-03-13 11:12:33.326133
Duration24.04 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84079602
Minimum-2
Maximum3
Zeros65
Zeros (%)32.3%
Negative25
Negative (%)12.4%
Memory size1.7 KiB
2025-03-13T16:42:33.376827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2548017
Coefficient of variation (CV)1.4923973
Kurtosis-0.70717762
Mean0.84079602
Median Absolute Deviation (MAD)1
Skewness0.19737036
Sum169
Variance1.5745274
MonotonicityNot monotonic
2025-03-13T16:42:33.461425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 65
32.3%
1 52
25.9%
2 32
15.9%
3 27
13.4%
-1 22
 
10.9%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.9%
0 65
32.3%
1 52
25.9%
2 32
15.9%
3 27
13.4%
ValueCountFrequency (%)
3 27
13.4%
2 32
15.9%
1 52
25.9%
0 65
32.3%
-1 22
 
10.9%
-2 3
 
1.5%

normalized-losses
Real number (ℝ)

Distinct51
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:33.545711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile77
Q1101
median122
Q3137
95-th percentile186
Maximum256
Range191
Interquartile range (IQR)36

Descriptive statistics

Standard deviation31.99625
Coefficient of variation (CV)0.26226434
Kurtosis1.3190676
Mean122
Median Absolute Deviation (MAD)21
Skewness0.84654635
Sum24522
Variance1023.76
MonotonicityNot monotonic
2025-03-13T16:42:33.643691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 41
20.4%
161 11
 
5.5%
91 8
 
4.0%
150 7
 
3.5%
128 6
 
3.0%
104 6
 
3.0%
134 6
 
3.0%
103 5
 
2.5%
168 5
 
2.5%
74 5
 
2.5%
Other values (41) 101
50.2%
ValueCountFrequency (%)
65 5
2.5%
74 5
2.5%
77 1
 
0.5%
78 1
 
0.5%
81 2
 
1.0%
83 3
1.5%
85 5
2.5%
87 2
 
1.0%
89 2
 
1.0%
90 1
 
0.5%
ValueCountFrequency (%)
256 1
 
0.5%
231 1
 
0.5%
197 2
 
1.0%
194 2
 
1.0%
192 2
 
1.0%
188 2
 
1.0%
186 1
 
0.5%
168 5
2.5%
164 2
 
1.0%
161 11
5.5%

make
Categorical

High correlation 

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
108 

Length

Max length13
Median length11
Mean length6.5024876
Min length3

Characters and Unicode

Total characters1307
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.9%
nissan 18
 
9.0%
mazda 17
 
8.5%
mitsubishi 13
 
6.5%
honda 13
 
6.5%
subaru 12
 
6.0%
volkswagen 12
 
6.0%
volvo 11
 
5.5%
peugot 11
 
5.5%
dodge 9
 
4.5%
Other values (12) 53
26.4%

Length

2025-03-13T16:42:33.739359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.9%
nissan 18
 
9.0%
mazda 17
 
8.5%
mitsubishi 13
 
6.5%
honda 13
 
6.5%
subaru 12
 
6.0%
volkswagen 12
 
6.0%
volvo 11
 
5.5%
peugot 11
 
5.5%
dodge 9
 
4.5%
Other values (12) 53
26.4%

Most occurring characters

ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
u 71
 
5.4%
n 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
u 71
 
5.4%
n 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
u 71
 
5.4%
n 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
u 71
 
5.4%
n 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

aspiration
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
std
165 
turbo
36 

Length

Max length5
Median length3
Mean length3.358209
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 165
82.1%
turbo 36
 
17.9%

Length

2025-03-13T16:42:33.828012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:33.926497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
std 165
82.1%
turbo 36
 
17.9%

Most occurring characters

ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

num-of-doors
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
four
115 
two
86 

Length

Max length4
Median length4
Mean length3.5721393
Min length3

Characters and Unicode

Total characters718
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 115
57.2%
two 86
42.8%

Length

2025-03-13T16:42:34.000518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:34.070142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
four 115
57.2%
two 86
42.8%

Most occurring characters

ValueCountFrequency (%)
o 201
28.0%
f 115
16.0%
u 115
16.0%
r 115
16.0%
t 86
12.0%
w 86
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 201
28.0%
f 115
16.0%
u 115
16.0%
r 115
16.0%
t 86
12.0%
w 86
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 201
28.0%
f 115
16.0%
u 115
16.0%
r 115
16.0%
t 86
12.0%
w 86
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 201
28.0%
f 115
16.0%
u 115
16.0%
r 115
16.0%
t 86
12.0%
w 86
12.0%

body-style
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
sedan
94 
hatchback
68 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6119403
Min length5

Characters and Unicode

Total characters1329
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 94
46.8%
hatchback 68
33.8%
wagon 25
 
12.4%
hardtop 8
 
4.0%
convertible 6
 
3.0%

Length

2025-03-13T16:42:34.147024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:34.226613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sedan 94
46.8%
hatchback 68
33.8%
wagon 25
 
12.4%
hardtop 8
 
4.0%
convertible 6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

drive-wheels
Categorical

High correlation 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
fwd
118 
rwd
75 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 118
58.7%
rwd 75
37.3%
4wd 8
 
4.0%

Length

2025-03-13T16:42:34.306556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:34.376182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
fwd 118
58.7%
rwd 75
37.3%
4wd 8
 
4.0%

Most occurring characters

ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

engine-location
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
front
198 
rear
 
3

Length

Max length5
Median length5
Mean length4.9850746
Min length4

Characters and Unicode

Total characters1002
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 198
98.5%
rear 3
 
1.5%

Length

2025-03-13T16:42:34.451017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:34.519549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
front 198
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.797015
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:34.596072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0663656
Coefficient of variation (CV)0.061402316
Kurtosis0.9484451
Mean98.797015
Median Absolute Deviation (MAD)2.8
Skewness1.0312614
Sum19858.2
Variance36.800791
MonotonicityNot monotonic
2025-03-13T16:42:34.695248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.7 20
 
10.0%
94.5 19
 
9.5%
95.7 13
 
6.5%
96.5 8
 
4.0%
97.3 7
 
3.5%
100.4 6
 
3.0%
96.3 6
 
3.0%
104.3 6
 
3.0%
107.9 6
 
3.0%
98.8 6
 
3.0%
Other values (42) 104
51.7%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.5%
93.3 1
 
0.5%
93.7 20
10.0%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.5%
108 1
 
0.5%
107.9 6
3.0%
106.7 1
 
0.5%

length
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83710233
Minimum0.6780394
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:34.794499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.6780394
5-th percentile0.75588659
Q10.80153772
median0.83229217
Q30.8817876
95-th percentile0.94666026
Maximum1
Range0.3219606
Interquartile range (IQR)0.08024988

Descriptive statistics

Standard deviation0.059212759
Coefficient of variation (CV)0.070735389
Kurtosis-0.065191628
Mean0.83710233
Median Absolute Deviation (MAD)0.033157136
Skewness0.15444635
Sum168.25757
Variance0.0035061508
MonotonicityNot monotonic
2025-03-13T16:42:34.894319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.755886593 15
 
7.5%
0.9072561269 11
 
5.5%
0.8971648246 7
 
3.5%
0.7991350312 7
 
3.5%
0.8250840942 7
 
3.5%
0.8467083133 6
 
3.0%
0.7943296492 6
 
3.0%
0.8543969246 6
 
3.0%
0.8966842864 6
 
3.0%
0.8495915425 5
 
2.5%
Other values (63) 125
62.2%
ValueCountFrequency (%)
0.6780394041 1
 
0.5%
0.6948582412 2
 
1.0%
0.7208073042 3
 
1.5%
0.7491590581 1
 
0.5%
0.7539644402 1
 
0.5%
0.7549255166 1
 
0.5%
0.755886593 15
7.5%
0.7587698222 1
 
0.5%
0.7626141278 3
 
1.5%
0.763094666 1
 
0.5%
ValueCountFrequency (%)
1 1
 
0.5%
0.9735703988 2
1.0%
0.9591542528 2
1.0%
0.9572321 1
 
0.5%
0.9557904853 4
2.0%
0.9466602595 1
 
0.5%
0.931283037 1
 
0.5%
0.9259971168 3
1.5%
0.9211917347 1
 
0.5%
0.9173474291 2
1.0%

width
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91512576
Minimum0.8375
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:34.988899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.8375
5-th percentile0.88333333
Q10.89027778
median0.90972222
Q30.925
95-th percentile0.97638889
Maximum1
Range0.1625
Interquartile range (IQR)0.034722222

Descriptive statistics

Standard deviation0.029187095
Coefficient of variation (CV)0.031894081
Kurtosis0.67865517
Mean0.91512576
Median Absolute Deviation (MAD)0.019444444
Skewness0.87502904
Sum183.94028
Variance0.0008518865
MonotonicityNot monotonic
2025-03-13T16:42:35.089319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.8861111111 24
 
11.9%
0.9236111111 23
 
11.4%
0.9083333333 15
 
7.5%
0.8944444444 10
 
5.0%
0.95 10
 
5.0%
0.8888888889 9
 
4.5%
0.8833333333 9
 
4.5%
0.9097222222 8
 
4.0%
0.9055555556 7
 
3.5%
0.9208333333 6
 
3.0%
Other values (33) 80
39.8%
ValueCountFrequency (%)
0.8375 1
 
0.5%
0.8583333333 1
 
0.5%
0.8680555556 1
 
0.5%
0.8805555556 1
 
0.5%
0.8833333333 9
 
4.5%
0.8861111111 24
11.9%
0.8875 3
 
1.5%
0.8888888889 9
 
4.5%
0.8902777778 2
 
1.0%
0.8916666667 6
 
3.0%
ValueCountFrequency (%)
1 1
 
0.5%
0.9958333333 3
1.5%
0.9916666667 3
1.5%
0.9847222222 1
 
0.5%
0.9805555556 1
 
0.5%
0.9791666667 1
 
0.5%
0.9763888889 3
1.5%
0.9666666667 2
1.0%
0.9569444444 4
2.0%
0.9555555556 1
 
0.5%

height
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.766667
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:35.190488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.4478222
Coefficient of variation (CV)0.045526761
Kurtosis-0.43290815
Mean53.766667
Median Absolute Deviation (MAD)1.6
Skewness0.029173299
Sum10807.1
Variance5.9918333
MonotonicityNot monotonic
2025-03-13T16:42:35.293813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
7.0%
55.7 12
 
6.0%
54.5 10
 
5.0%
54.1 10
 
5.0%
52 9
 
4.5%
55.5 9
 
4.5%
56.7 8
 
4.0%
54.3 8
 
4.0%
52.6 7
 
3.5%
51.6 7
 
3.5%
Other values (39) 107
53.2%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
3.0%
50.5 1
 
0.5%
50.6 5
 
2.5%
50.8 14
7.0%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
4.0%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.5%

curb-weight
Real number (ℝ)

High correlation 

Distinct169
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.6667
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:35.394510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1905
Q12169
median2414
Q32926
95-th percentile3505
Maximum4066
Range2578
Interquartile range (IQR)757

Descriptive statistics

Standard deviation517.29673
Coefficient of variation (CV)0.20241166
Kurtosis0.034915576
Mean2555.6667
Median Absolute Deviation (MAD)377
Skewness0.70580359
Sum513689
Variance267595.9
MonotonicityNot monotonic
2025-03-13T16:42:35.496278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2337 2
 
1.0%
1967 2
 
1.0%
2395 2
 
1.0%
2535 2
 
1.0%
2128 2
 
1.0%
4066 2
 
1.0%
Other values (159) 176
87.6%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 1
0.5%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

engine-type
Categorical

High correlation 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
ohc
145 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12

Length

Max length5
Median length3
Mean length3.119403
Min length1

Characters and Unicode

Total characters627
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 145
72.1%
ohcf 15
 
7.5%
ohcv 13
 
6.5%
dohc 12
 
6.0%
l 12
 
6.0%
rotor 4
 
2.0%

Length

2025-03-13T16:42:35.600006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:35.686868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ohc 145
72.1%
ohcf 15
 
7.5%
ohcv 13
 
6.5%
dohc 12
 
6.0%
l 12
 
6.0%
rotor 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

num-of-cylinders
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
four
157 
six
24 
five
 
10
eight
 
4
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.8955224
Min length3

Characters and Unicode

Total characters783
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 157
78.1%
six 24
 
11.9%
five 10
 
5.0%
eight 4
 
2.0%
two 4
 
2.0%
twelve 1
 
0.5%
three 1
 
0.5%

Length

2025-03-13T16:42:35.775230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:35.856009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
four 157
78.1%
six 24
 
11.9%
five 10
 
5.0%
eight 4
 
2.0%
two 4
 
2.0%
twelve 1
 
0.5%
three 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

engine-size
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.87562
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:35.948318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3141
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)43

Descriptive statistics

Standard deviation41.546834
Coefficient of variation (CV)0.32746113
Kurtosis5.4974908
Mean126.87562
Median Absolute Deviation (MAD)22
Skewness1.9791442
Sum25502
Variance1726.1395
MonotonicityNot monotonic
2025-03-13T16:42:36.047154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
122 15
 
7.5%
92 15
 
7.5%
98 14
 
7.0%
97 14
 
7.0%
108 13
 
6.5%
110 12
 
6.0%
90 10
 
5.0%
109 8
 
4.0%
120 7
 
3.5%
141 7
 
3.5%
Other values (33) 86
42.8%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 10
5.0%
91 5
 
2.5%
92 15
7.5%
97 14
7.0%
98 14
7.0%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
194 3
1.5%
183 4
2.0%
181 6
3.0%
173 1
 
0.5%

fuel-system
Categorical

High correlation 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
mpfi
92 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8955224
Min length3

Characters and Unicode

Total characters783
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 92
45.8%
2bbl 64
31.8%
idi 20
 
10.0%
1bbl 11
 
5.5%
spdi 9
 
4.5%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2025-03-13T16:42:36.144039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:36.228920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 92
45.8%
2bbl 64
31.8%
idi 20
 
10.0%
1bbl 11
 
5.5%
spdi 9
 
4.5%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

bore
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3306916
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:36.321731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.26807186
Coefficient of variation (CV)0.080485344
Kurtosis-0.79819313
Mean3.3306916
Median Absolute Deviation (MAD)0.23
Skewness-0.032730328
Sum669.469
Variance0.071862521
MonotonicityNot monotonic
2025-03-13T16:42:36.414746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3.62 23
 
11.4%
3.19 20
 
10.0%
3.15 15
 
7.5%
2.97 12
 
6.0%
3.03 10
 
5.0%
3.46 9
 
4.5%
3.31 8
 
4.0%
3.43 8
 
4.0%
3.78 8
 
4.0%
3.27 7
 
3.5%
Other values (29) 81
40.3%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.5%
2.92 1
 
0.5%
2.97 12
6.0%
2.99 1
 
0.5%
3.01 5
2.5%
3.03 10
5.0%
3.05 6
3.0%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 1
 
0.5%
3.8 2
 
1.0%
3.78 8
 
4.0%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.5%
3.63 2
 
1.0%
3.62 23
11.4%
3.61 1
 
0.5%
3.6 1
 
0.5%

stroke
Real number (ℝ)

High correlation  Missing 

Distinct36
Distinct (%)18.3%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.2569036
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:36.509180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31925624
Coefficient of variation (CV)0.098024469
Kurtosis2.0287842
Mean3.2569036
Median Absolute Deviation (MAD)0.17
Skewness-0.69377839
Sum641.61
Variance0.10192455
MonotonicityNot monotonic
2025-03-13T16:42:36.598402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 19
 
9.5%
3.23 14
 
7.0%
3.15 14
 
7.0%
3.03 14
 
7.0%
3.39 13
 
6.5%
2.64 11
 
5.5%
3.35 9
 
4.5%
3.29 9
 
4.5%
3.46 8
 
4.0%
3.27 6
 
3.0%
Other values (26) 80
39.8%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.5%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
7.0%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.5%
3.58 6
3.0%
3.54 4
2.0%
3.52 5
2.5%
3.5 6
3.0%
3.47 4
2.0%
3.46 8
4.0%

compression-ratio
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.164279
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:36.679227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.9
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.0049655
Coefficient of variation (CV)0.39402358
Kurtosis5.0688725
Mean10.164279
Median Absolute Deviation (MAD)0.4
Skewness2.5844624
Sum2043.02
Variance16.039749
MonotonicityNot monotonic
2025-03-13T16:42:36.765175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.9%
9.4 26
12.9%
8.5 14
 
7.0%
9.5 13
 
6.5%
9.3 11
 
5.5%
8.7 9
 
4.5%
9.2 8
 
4.0%
8 8
 
4.0%
7 6
 
3.0%
23 5
 
2.5%
Other values (22) 55
27.4%
ValueCountFrequency (%)
7 6
3.0%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
4.0%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
7.0%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 2
 
1.0%

horsepower
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.40553
Minimum48
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:36.861233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile176
Maximum262
Range214
Interquartile range (IQR)46

Descriptive statistics

Standard deviation37.3657
Coefficient of variation (CV)0.36135106
Kurtosis1.3203795
Mean103.40553
Median Absolute Deviation (MAD)25
Skewness1.1465173
Sum20784.512
Variance1396.1955
MonotonicityNot monotonic
2025-03-13T16:42:36.957477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.5%
69 10
 
5.0%
70 9
 
4.5%
116 9
 
4.5%
110 8
 
4.0%
95 7
 
3.5%
88 6
 
3.0%
62 6
 
3.0%
101 6
 
3.0%
114 6
 
3.0%
Other values (49) 115
57.2%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.0%
64 1
 
0.5%
68 19
9.5%
69 10
5.0%
ValueCountFrequency (%)
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
 
1.0%
182 3
1.5%
176 2
 
1.0%
175 1
 
0.5%
162 2
 
1.0%
161 2
 
1.0%
160 5
2.5%

peak-rpm
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5117.6654
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:37.039943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5125.3695
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation478.11381
Coefficient of variation (CV)0.093424202
Kurtosis0.10755082
Mean5117.6654
Median Absolute Deviation (MAD)325.36946
Skewness0.10776997
Sum1028650.7
Variance228592.81
MonotonicityNot monotonic
2025-03-13T16:42:37.119918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 36
17.9%
4800 36
17.9%
5000 27
13.4%
5200 23
11.4%
5400 11
 
5.5%
6000 9
 
4.5%
5800 7
 
3.5%
4500 7
 
3.5%
5250 7
 
3.5%
4150 5
 
2.5%
Other values (13) 33
16.4%
ValueCountFrequency (%)
4150 5
 
2.5%
4200 5
 
2.5%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.5%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.9%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.5%
5900 3
 
1.5%
5800 7
 
3.5%
5600 1
 
0.5%
5500 36
17.9%
5400 11
 
5.5%
5300 1
 
0.5%
5250 7
 
3.5%
5200 23
11.4%

city-mpg
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.179104
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:37.196879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.4232205
Coefficient of variation (CV)0.25510123
Kurtosis0.75396809
Mean25.179104
Median Absolute Deviation (MAD)5
Skewness0.68043347
Sum5061
Variance41.257761
MonotonicityNot monotonic
2025-03-13T16:42:37.286449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.9%
19 27
13.4%
24 22
10.9%
27 14
 
7.0%
23 12
 
6.0%
26 12
 
6.0%
17 12
 
6.0%
21 8
 
4.0%
25 8
 
4.0%
30 8
 
4.0%
Other values (19) 50
24.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 5
 
2.5%
17 12
6.0%
18 3
 
1.5%
19 27
13.4%
20 3
 
1.5%
21 8
 
4.0%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 5
2.5%
37 6
3.0%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway-mpg
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.686567
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:37.373221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8151499
Coefficient of variation (CV)0.22208903
Kurtosis0.56117114
Mean30.686567
Median Absolute Deviation (MAD)5
Skewness0.54950715
Sum6168
Variance46.446269
MonotonicityNot monotonic
2025-03-13T16:42:37.464822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.5%
24 17
 
8.5%
38 17
 
8.5%
30 16
 
8.0%
32 16
 
8.0%
34 14
 
7.0%
37 13
 
6.5%
28 12
 
6.0%
29 10
 
5.0%
33 9
 
4.5%
Other values (20) 58
28.9%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 7
 
3.5%
23 7
 
3.5%
24 17
8.5%
25 19
9.5%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 2
 
1.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.5%

price
Real number (ℝ)

High correlation 

Distinct186
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13207.129
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:37.560054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.0663
Coefficient of variation (CV)0.60172549
Kurtosis3.2315369
Mean13207.129
Median Absolute Deviation (MAD)3306
Skewness1.8096753
Sum2654633
Variance63155863
MonotonicityNot monotonic
2025-03-13T16:42:37.665749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16500 2
 
1.0%
6229 2
 
1.0%
7609 2
 
1.0%
7957 2
 
1.0%
6692 2
 
1.0%
5572 2
 
1.0%
8495 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
8921 2
 
1.0%
Other values (176) 181
90.0%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

city-L/100km
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9441455
Minimum4.7959184
Maximum18.076923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:37.762216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.7959184
5-th percentile6.3513514
Q17.8333333
median9.7916667
Q312.368421
95-th percentile14.6875
Maximum18.076923
Range13.281005
Interquartile range (IQR)4.5350877

Descriptive statistics

Standard deviation2.5345993
Coefficient of variation (CV)0.25488357
Kurtosis-0.065118888
Mean9.9441455
Median Absolute Deviation (MAD)1.9583333
Skewness0.59238336
Sum1998.7732
Variance6.4241934
MonotonicityNot monotonic
2025-03-13T16:42:37.854696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
7.580645161 28
13.9%
12.36842105 27
13.4%
9.791666667 22
10.9%
8.703703704 14
 
7.0%
10.2173913 12
 
6.0%
9.038461538 12
 
6.0%
13.82352941 12
 
6.0%
11.19047619 8
 
4.0%
9.4 8
 
4.0%
7.833333333 8
 
4.0%
Other values (19) 50
24.9%
ValueCountFrequency (%)
4.795918367 1
 
0.5%
5 1
 
0.5%
5.222222222 1
 
0.5%
6.184210526 5
2.5%
6.351351351 6
3.0%
6.527777778 1
 
0.5%
6.714285714 1
 
0.5%
6.911764706 1
 
0.5%
7.121212121 1
 
0.5%
7.34375 1
 
0.5%
ValueCountFrequency (%)
18.07692308 1
 
0.5%
16.78571429 2
 
1.0%
15.66666667 3
 
1.5%
14.6875 5
 
2.5%
13.82352941 12
6.0%
13.05555556 3
 
1.5%
12.36842105 27
13.4%
11.75 3
 
1.5%
11.19047619 8
 
4.0%
10.68181818 4
 
2.0%

horsepower-binned
Categorical

High correlation 

Distinct3
Distinct (%)1.5%
Missing1
Missing (%)0.5%
Memory size12.1 KiB
Low
115 
Medium
62 
High
23 

Length

Max length6
Median length3
Mean length4.045
Min length3

Characters and Unicode

Total characters809
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Low 115
57.2%
Medium 62
30.8%
High 23
 
11.4%
(Missing) 1
 
0.5%

Length

2025-03-13T16:42:37.948322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:38.024372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
low 115
57.5%
medium 62
31.0%
high 23
 
11.5%

Most occurring characters

ValueCountFrequency (%)
L 115
14.2%
o 115
14.2%
w 115
14.2%
i 85
10.5%
M 62
7.7%
e 62
7.7%
d 62
7.7%
u 62
7.7%
m 62
7.7%
H 23
 
2.8%
Other values (2) 46
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 115
14.2%
o 115
14.2%
w 115
14.2%
i 85
10.5%
M 62
7.7%
e 62
7.7%
d 62
7.7%
u 62
7.7%
m 62
7.7%
H 23
 
2.8%
Other values (2) 46
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 115
14.2%
o 115
14.2%
w 115
14.2%
i 85
10.5%
M 62
7.7%
e 62
7.7%
d 62
7.7%
u 62
7.7%
m 62
7.7%
H 23
 
2.8%
Other values (2) 46
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 115
14.2%
o 115
14.2%
w 115
14.2%
i 85
10.5%
M 62
7.7%
e 62
7.7%
d 62
7.7%
u 62
7.7%
m 62
7.7%
H 23
 
2.8%
Other values (2) 46
 
5.7%

diesel
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
181 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters201
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 181
90.0%
1 20
 
10.0%

Length

2025-03-13T16:42:38.101335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:38.170715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 181
90.0%
1 20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 181
90.0%
1 20
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 181
90.0%
1 20
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 181
90.0%
1 20
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 181
90.0%
1 20
 
10.0%

gas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
181 
0
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters201
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 181
90.0%
0 20
 
10.0%

Length

2025-03-13T16:42:38.240741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T16:42:38.308810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 181
90.0%
0 20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 181
90.0%
0 20
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 181
90.0%
0 20
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 181
90.0%
0 20
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 181
90.0%
0 20
 
10.0%

engine_hp_ratio
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2689077
Minimum0.59259259
Maximum1.9642857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-03-13T16:42:38.386627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.59259259
5-th percentile0.875
Q11.1483516
median1.2790698
Q31.4057971
95-th percentile1.6
Maximum1.9642857
Range1.3716931
Interquartile range (IQR)0.25744545

Descriptive statistics

Standard deviation0.23453511
Coefficient of variation (CV)0.18483229
Kurtosis1.0455887
Mean1.2689077
Median Absolute Deviation (MAD)0.12672733
Skewness0.03263383
Sum255.05044
Variance0.055006718
MonotonicityNot monotonic
2025-03-13T16:42:38.488428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.405797101 10
 
5.0%
1.323529412 8
 
4.0%
1.4 7
 
3.5%
1.483870968 6
 
3.0%
1.25862069 6
 
3.0%
1.236842105 6
 
3.0%
1.210526316 5
 
2.5%
1.1 5
 
2.5%
1.075862069 5
 
2.5%
1.386363636 5
 
2.5%
Other values (68) 138
68.7%
ValueCountFrequency (%)
0.5925925926 1
 
0.5%
0.6930693069 3
1.5%
0.75625 2
1.0%
0.8 1
 
0.5%
0.8024691358 2
1.0%
0.875 2
1.0%
0.88125 1
 
0.5%
0.905 1
 
0.5%
0.9357142857 1
 
0.5%
0.9371980676 3
1.5%
ValueCountFrequency (%)
1.964285714 2
 
1.0%
1.90625 1
 
0.5%
1.872727273 1
 
0.5%
1.865384615 2
 
1.0%
1.861111111 1
 
0.5%
1.673913043 1
 
0.5%
1.652173913 1
 
0.5%
1.6 5
2.5%
1.586206897 1
 
0.5%
1.509677419 2
 
1.0%

Interactions

2025-03-13T16:42:31.263189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.521481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.633050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.791268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.984765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.012240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.176952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.477002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.566714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.657550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.062324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.130725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.259138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.315931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.697621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.831365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.986076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.157917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.327684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.579276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.694115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.852449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:14.047785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.074322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.234921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.534559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.624917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.719793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.118281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.191777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.314461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.371490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.757314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.891950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.047245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.215940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.393720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.641346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.758680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.919132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:14.119457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.140902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.297347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.599064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.686034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.788752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.178511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.257707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.373296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.433532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.823549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.958990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.114865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.278655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.460062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.703719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.824389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.985440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.075013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.207876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.362289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.663281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.749098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.856420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.239726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.322509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.433304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.493977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.888745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.025375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.184083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.341501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.522942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.765250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.887167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.048580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.137606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.270611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.423246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.722873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.809768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.919370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.298561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.384556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.490608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.553412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.950750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.091143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.250734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.402673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.593700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.830824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.957885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.120459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.207860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.337442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.487836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.787463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.876700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.987430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.363204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.450975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.554636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.616624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.019541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.160447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.320690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.467063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.656225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.896775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.019471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.185244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.268288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.400550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.546790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.846446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.937657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.049984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.422965image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.511661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.613153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.674657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.087539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.223222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.384332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.527874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.112507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:10.954189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.081226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.245343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.329388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.462403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.604328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.903251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.995921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.111251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.477552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.571878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.668724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.731269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.148174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.286043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.446058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.585964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.175050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.012204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.141224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-13T16:42:15.390889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.527695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.662592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.960168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.053700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.170101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-13T16:42:23.632263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.724125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.787598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.206780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.347010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.507110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.644792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.240399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.074390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.210506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.377621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-13T16:42:17.725336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-13T16:42:20.116925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.234749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-13T16:42:23.696508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.784064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-13T16:42:32.296639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.131283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.270122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.441800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.516516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.653103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.780898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.077450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.171611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.293996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.647341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.754757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.836832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.900731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.328920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.471071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.631966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.768907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.362456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.196255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.336564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.512462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.580110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.719584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.843830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.139856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.233397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.360472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.708159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.820097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.898950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.963675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.393245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.537765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.700030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.831329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.421911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.253763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.394400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.581717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.635787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.778201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.100686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.193570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.287540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.418446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.763028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.878049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.952734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.017126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.450094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.595598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.763203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.886654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.482491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.309844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.453402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.643671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.691540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.836063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.157452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.248452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.342348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.478484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.823002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.933465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.008831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.069115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.507098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.654899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.821832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.940983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.547451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.374483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.522053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.712646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.754991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.901424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.221285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.311248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.404721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.542683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.886647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.997900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.071184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.449332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.569867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.719404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.888463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.004067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.616601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.439984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.590638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.781055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.819934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:16.970119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.286408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.376246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.468726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.868104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:22.950516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.065676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.134738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.513089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.635468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.786623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:29.958859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.070652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.684465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.508308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.661542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.851374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.886723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.039850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.352502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.442285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.533736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.934216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.013657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.133194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.199317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.577532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.702960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.855975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.026745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.138157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:32.746735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:11.569820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:12.724520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:13.918581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:15.948725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:17.105245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:18.413760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:19.502692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:20.594541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:21.996959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:23.072879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:24.194452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:25.255904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:26.635608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:27.765225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:28.919157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:30.090209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-13T16:42:31.198771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-13T16:42:38.588633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
aspirationbody-styleborecity-L/100kmcity-mpgcompression-ratiocurb-weightdieseldrive-wheelsengine-locationengine-sizeengine-typeengine_hp_ratiofuel-systemgasheighthighway-mpghorsepowerhorsepower-binnedlengthmakenormalized-lossesnum-of-cylindersnum-of-doorspeak-rpmpricestrokesymbolingwheel-basewidth
aspiration1.0000.0000.3460.2110.2110.5510.3720.3810.0920.0000.2720.1650.6150.6170.3810.2540.3070.3400.3210.2180.4090.0000.1530.0000.3110.3940.2540.1870.3160.320
body-style0.0001.0000.1500.0880.0000.0530.2440.1740.2300.4370.2150.1400.1070.1540.1740.5010.0000.2070.1260.2550.3310.1550.1100.7530.0700.2400.1540.3370.3390.147
bore0.3460.1501.0000.607-0.607-0.1690.7020.1690.4340.3250.6970.336-0.1520.3410.1690.223-0.6190.6410.3560.6400.516-0.0220.2240.135-0.3050.646-0.083-0.1800.5380.607
city-L/100km0.2110.0880.6071.000-1.000-0.4760.8060.3250.3820.3790.7220.368-0.5600.3180.3250.080-0.9690.9110.6510.6610.3950.2590.5230.0690.1290.8310.0240.0220.4840.673
city-mpg0.2110.000-0.607-1.0001.0000.476-0.8060.4030.3810.112-0.7220.2450.5600.3040.403-0.0800.969-0.9110.615-0.6610.360-0.2590.4470.000-0.129-0.831-0.024-0.022-0.484-0.673
compression-ratio0.5510.053-0.169-0.4760.4761.000-0.2130.9920.1050.000-0.2330.3440.3170.5180.9920.0080.434-0.3560.177-0.1800.489-0.0680.5250.194-0.027-0.178-0.0530.021-0.120-0.139
curb-weight0.3720.2440.7020.806-0.806-0.2131.0000.3280.4520.0940.8740.338-0.1950.2910.3280.363-0.8310.8040.5510.8900.4960.1230.5000.289-0.2470.9140.157-0.2610.7650.859
diesel0.3810.1740.1690.3250.4030.9920.3281.0000.0850.0000.1510.2580.7210.9850.9720.2720.3590.1180.1000.1020.3650.1030.1740.1550.5920.3400.3710.2190.3390.267
drive-wheels0.0920.2300.4340.3820.3810.1050.4520.0851.0000.1250.4670.4400.2860.3860.0850.3810.4240.4450.3620.4100.6050.2440.3140.0970.2530.4430.3570.2650.4120.411
engine-location0.0000.4370.3250.3790.1120.0000.0940.0000.1251.0000.6740.4050.3250.0000.0000.3040.0950.8420.3280.0000.8030.0000.2870.0720.4470.4680.6130.2700.5680.078
engine-size0.2720.2150.6970.722-0.722-0.2330.8740.1510.4670.6741.0000.559-0.1270.3300.1510.209-0.7170.8180.6250.7800.5240.1240.6640.208-0.2830.8280.293-0.1820.6460.763
engine-type0.1650.1400.3360.3680.2450.3440.3380.2580.4400.4050.5591.0000.5210.4190.2580.4310.3670.4350.4010.3510.6750.2980.5770.2010.3680.2610.4950.2290.3920.415
engine_hp_ratio0.6150.107-0.152-0.5600.5600.317-0.1950.7210.2860.325-0.1270.5211.0000.5130.7210.2530.505-0.5900.538-0.0790.369-0.2290.4300.184-0.672-0.3210.100-0.2580.037-0.148
fuel-system0.6170.1540.3410.3180.3040.5180.2910.9850.3860.0000.3300.4190.5131.0000.9850.2950.3440.3350.5420.3220.5410.1080.3720.2520.3680.2870.3260.2680.2230.259
gas0.3810.1740.1690.3250.4030.9920.3280.9720.0850.0000.1510.2580.7210.9851.0000.2720.3590.1180.1000.1020.3650.1030.1740.1550.5920.3400.3710.2190.3390.267
height0.2540.5010.2230.080-0.0800.0080.3630.2720.3810.3040.2090.4310.2530.2950.2721.000-0.1380.0220.2430.5320.484-0.3460.3540.528-0.2800.264-0.037-0.5300.6410.371
highway-mpg0.3070.000-0.619-0.9690.9690.434-0.8310.3590.4240.095-0.7170.3670.5050.3440.359-0.1381.000-0.8860.591-0.6890.401-0.2090.5210.095-0.058-0.827-0.0200.050-0.531-0.692
horsepower0.3400.2070.6410.911-0.911-0.3560.8040.1180.4450.8420.8180.435-0.5900.3350.1180.022-0.8861.0000.9080.6600.4580.2370.5300.1280.1030.8490.139-0.0040.4950.681
horsepower-binned0.3210.1260.3560.6510.6150.1770.5510.1000.3620.3280.6250.4010.5380.5420.1000.2430.5910.9081.0000.4100.5160.2220.5130.0300.2280.5990.2590.2680.3800.416
length0.2180.2550.6400.661-0.661-0.1800.8900.1020.4100.0000.7800.351-0.0790.3220.1020.532-0.6890.6600.4101.0000.5010.0460.3710.356-0.2720.8100.173-0.4040.9130.890
make0.4090.3310.5160.3950.3600.4890.4960.3650.6050.8030.5240.6750.3690.5410.3650.4840.4010.4580.5160.5011.0000.3380.5490.2970.4770.3700.5860.4550.5130.553
normalized-losses0.0000.155-0.0220.259-0.259-0.0680.1230.1030.2440.0000.1240.298-0.2290.1080.103-0.346-0.2090.2370.2220.0460.3381.0000.1950.3740.2410.2020.1090.484-0.0730.118
num-of-cylinders0.1530.1100.2240.5230.4470.5250.5000.1740.3140.2870.6640.5770.4300.3720.1740.3540.5210.5300.5130.3710.5490.1951.0000.1490.2830.4470.2520.1620.3380.565
num-of-doors0.0000.7530.1350.0690.0000.1940.2890.1550.0970.0720.2080.2010.1840.2520.1550.5280.0950.1280.0300.3560.2970.3740.1491.0000.2490.0000.1500.6870.4470.245
peak-rpm0.3110.070-0.3050.129-0.129-0.027-0.2470.5920.2530.447-0.2830.368-0.6720.3680.592-0.280-0.0580.1030.228-0.2720.4770.2410.2830.2491.000-0.082-0.0670.290-0.316-0.214
price0.3940.2400.6460.831-0.831-0.1780.9140.3400.4430.4680.8280.261-0.3210.2870.3400.264-0.8270.8490.5990.8100.3700.2020.4470.000-0.0821.0000.118-0.1430.6820.812
stroke0.2540.154-0.0830.024-0.024-0.0530.1570.3710.3570.6130.2930.4950.1000.3260.371-0.037-0.0200.1390.2590.1730.5860.1090.2520.150-0.0670.1181.000-0.0120.2180.238
symboling0.1870.337-0.1800.022-0.0220.021-0.2610.2190.2650.270-0.1820.229-0.2580.2680.219-0.5300.050-0.0040.268-0.4040.4550.4840.1620.6870.290-0.143-0.0121.000-0.542-0.261
wheel-base0.3160.3390.5380.484-0.484-0.1200.7650.3390.4120.5680.6460.3920.0370.2230.3390.641-0.5310.4950.3800.9130.513-0.0730.3380.447-0.3160.6820.218-0.5421.0000.816
width0.3200.1470.6070.673-0.673-0.1390.8590.2670.4110.0780.7630.415-0.1480.2590.2670.371-0.6920.6810.4160.8900.5530.1180.5650.245-0.2140.8120.238-0.2610.8161.000

Missing values

2025-03-13T16:42:32.871073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-13T16:42:33.143330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-13T16:42:33.276388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhorsepower-binneddieselgasengine_hp_ratio
03122alfa-romerostdtwoconvertiblerwdfront88.60.8111480.89027848.82548dohcfour130mpfi3.472.689.0111.05000.0212713495.011.190476Medium011.171171
13122alfa-romerostdtwoconvertiblerwdfront88.60.8111480.89027848.82548dohcfour130mpfi3.472.689.0111.05000.0212716500.011.190476Medium011.171171
21122alfa-romerostdtwohatchbackrwdfront94.50.8226810.90972252.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500.012.368421Medium010.987013
32164audistdfoursedanfwdfront99.80.8486300.91944454.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950.09.791667Medium011.068627
42164audistdfoursedan4wdfront99.40.8486300.92222254.32824ohcfive136mpfi3.193.408.0115.05500.0182217450.013.055556Medium011.182609
52122audistdtwosedanfwdfront99.80.8519940.92083353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250.012.368421Medium011.236364
61158audistdfoursedanfwdfront105.80.9259970.99166755.72844ohcfive136mpfi3.193.408.5110.05500.0192517710.012.368421Medium011.236364
71122audistdfourwagonfwdfront105.80.9259970.99166755.72954ohcfive136mpfi3.193.408.5110.05500.0192518920.012.368421Medium011.236364
81158auditurbofoursedanfwdfront105.80.9259970.99166755.93086ohcfive131mpfi3.133.408.3140.05500.0172023875.013.823529Medium010.935714
92192bmwstdtwosedanrwdfront101.20.8495920.90000054.32395ohcfour108mpfi3.502.808.8101.05800.0232916430.010.217391Low011.069307
symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhorsepower-binneddieselgasengine_hp_ratio
191-174volvostdfourwagonrwdfront104.30.9072560.93333357.53034ohcfour141mpfi3.783.159.5114.05400.0232813415.010.217391Medium011.236842
192-2103volvostdfoursedanrwdfront104.30.9072560.93333356.22935ohcfour141mpfi3.783.159.5114.05400.0242815985.09.791667Medium011.236842
193-174volvostdfourwagonrwdfront104.30.9072560.93333357.53042ohcfour141mpfi3.783.159.5114.05400.0242816515.09.791667Medium011.236842
194-2103volvoturbofoursedanrwdfront104.30.9072560.93333356.23045ohcfour130mpfi3.623.157.5162.05100.0172218420.013.823529High010.802469
195-174volvoturbofourwagonrwdfront104.30.9072560.93333357.53157ohcfour130mpfi3.623.157.5162.05100.0172218950.013.823529High010.802469
196-195volvostdfoursedanrwdfront109.10.9072560.95694455.52952ohcfour141mpfi3.783.159.5114.05400.0232816845.010.217391Medium011.236842
197-195volvoturbofoursedanrwdfront109.10.9072560.95555655.53049ohcfour141mpfi3.783.158.7160.05300.0192519045.012.368421High010.881250
198-195volvostdfoursedanrwdfront109.10.9072560.95694455.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485.013.055556Medium011.291045
199-195volvoturbofoursedanrwdfront109.10.9072560.95694455.53217ohcsix145idi3.013.4023.0106.04800.0262722470.09.038462Medium101.367925
200-195volvoturbofoursedanrwdfront109.10.9072560.95694455.53062ohcfour141mpfi3.783.159.5114.05400.0192522625.012.368421Medium011.236842